A quantum alternating operator ansatz with hard and soft constraints for lattice protein folding

TL;DR

This paper introduces a novel quantum algorithm for lattice protein folding on universal gate-based quantum computers, utilizing a quantum alternating operator ansatz with hard and soft constraints to improve solution sampling.

Contribution

It presents a new quantum algorithm framework for lattice protein folding that incorporates hard and soft constraints within a variational quantum approach.

Findings

Proposes a quantum alternating operator ansatz for protein folding.

Uses hard and soft constraints to enhance sampling of ground states.

Adapts variational algorithms for lattice protein models.

Abstract

Gate-based universal quantum computers form a rapidly evolving field of quantum computing hardware technology. In previous work, we presented a quantum algorithm for lattice protein folding on a cubic lattice, tailored for quantum annealers. In this paper, we introduce a novel approach for solving the lattice protein folding problem on universal gate-based quantum computing architectures. Lattice protein models are coarse-grained representations of proteins that have been used extensively over the past thirty years to examine the principles of protein folding and design.These models can be used to explore a vast number of possible protein conformations and to infer structural properties of more complex atomistic protein structures. We formulate the problem as a quantum alternating operator ansatz, a member of the wider class of variational quantum/classical hybrid algorithms. To increase the probability of sampling the ground state, we propose splitting the optimization problem into hard and soft constraints. This enables us to use a previously under-utilised component of the variational algorithm to constrain the search to the subspace of solutions that satisfy the hard constraints.